A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting.
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| Title: | A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. |
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| Authors: | Liu, Zhi-Feng1 (AUTHOR) liuzhifeng@tust.edu.cn, Chen, Xiao-Rui1 (AUTHOR) 21021127@mail.tust.edu.cn, Huang, Ya-He1 (AUTHOR) huangyahe@mail.tust.edu.cn, Luo, Xing-Fu1 (AUTHOR) fu406699@mail.tust.edu.cn, Zhang, Shu-Rui1 (AUTHOR) zsr030923@mail.tust.edu.cn, You, Guo-Dong1 (AUTHOR) yougdtust@126.com, Qiang, Xiao-Yong2 (AUTHOR), Kang, Qing2 (AUTHOR) kq1301@163.com |
| Source: | Energy. Sep2024, Vol. 303, pN.PAG-N.PAG. 1p. |
| Subjects: | Deep learning, Outlier detection, Differential evolution, Optimization algorithms, Forecasting, Carbon offsetting, Carbon emissions |
| Abstract: | Under the goals of carbon neutrality and peak carbon emissions, photovoltaic (PV) power generation is widely valued for its clean and green characteristics. However, the uncertainty and randomness of PV power pose challenges to energy management. Therefore, this study proposed a novel bimodal feature fusion network-based deep learning model with an intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. First, a threshold-guided iNNE-based outlier detection and repair method is designed for preprocessing PV data. Second, a bimodal feature fusion network was proposed to extract global and local features from PV power sequences, and the environmental factors-based rime optimization algorithm with growth mutation strategy and humidity perception mechanism was devised to optimize model's hyperparameters. Additionally, a photovoltaic power interval prediction model with a volatility segmentation strategy was introduced. Finally, the effectiveness of the proposed model, algorithm, and strategies was validated using measured datasets. The results demonstrated that under various weather conditions, the proposed model achieved point prediction evaluation metrics with an R2 exceeding 98 % and a prediction interval evaluation metric with a Prediction Interval Coverage Probability of 85.07 %. The obtained outcomes contribute to providing a basis for decision-making in the scientific scheduling and management of PV power systems. • The proposal of a bimodal feature fusion network-based deep learning model. • The proposal of a threshold-guided iNNE-based outlier detection and repair method. • The proposal of EFRIME with growth mutation and humidity perception mechanism. • The introduction of the TAP-MSIP model with the VSS strategy. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177907059 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Zhi-Feng%22">Liu, Zhi-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuzhifeng@tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiao-Rui%22">Chen, Xiao-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 21021127@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Ya-He%22">Huang, Ya-He</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huangyahe@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Xing-Fu%22">Luo, Xing-Fu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fu406699@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shu-Rui%22">Zhang, Shu-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zsr030923@mail.tust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22You%2C+Guo-Dong%22">You, Guo-Dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yougdtust@126.com</i><br /><searchLink fieldCode="AR" term="%22Qiang%2C+Xiao-Yong%22">Qiang, Xiao-Yong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kang%2C+Qing%22">Kang, Qing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> kq1301@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Sep2024, Vol. 303, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+offsetting%22">Carbon offsetting</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Under the goals of carbon neutrality and peak carbon emissions, photovoltaic (PV) power generation is widely valued for its clean and green characteristics. However, the uncertainty and randomness of PV power pose challenges to energy management. Therefore, this study proposed a novel bimodal feature fusion network-based deep learning model with an intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. First, a threshold-guided iNNE-based outlier detection and repair method is designed for preprocessing PV data. Second, a bimodal feature fusion network was proposed to extract global and local features from PV power sequences, and the environmental factors-based rime optimization algorithm with growth mutation strategy and humidity perception mechanism was devised to optimize model's hyperparameters. Additionally, a photovoltaic power interval prediction model with a volatility segmentation strategy was introduced. Finally, the effectiveness of the proposed model, algorithm, and strategies was validated using measured datasets. The results demonstrated that under various weather conditions, the proposed model achieved point prediction evaluation metrics with an R2 exceeding 98 % and a prediction interval evaluation metric with a Prediction Interval Coverage Probability of 85.07 %. The obtained outcomes contribute to providing a basis for decision-making in the scientific scheduling and management of PV power systems. • The proposal of a bimodal feature fusion network-based deep learning model. • The proposal of a threshold-guided iNNE-based outlier detection and repair method. • The proposal of EFRIME with growth mutation and humidity perception mechanism. • The introduction of the TAP-MSIP model with the VSS strategy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.energy.2024.131947 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Differential evolution Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Carbon offsetting Type: general – SubjectFull: Carbon emissions Type: general Titles: – TitleFull: A novel bimodal feature fusion network-based deep learning model with intelligent fusion gate mechanism for short-term photovoltaic power point-interval forecasting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Zhi-Feng – PersonEntity: Name: NameFull: Chen, Xiao-Rui – PersonEntity: Name: NameFull: Huang, Ya-He – PersonEntity: Name: NameFull: Luo, Xing-Fu – PersonEntity: Name: NameFull: Zhang, Shu-Rui – PersonEntity: Name: NameFull: You, Guo-Dong – PersonEntity: Name: NameFull: Qiang, Xiao-Yong – PersonEntity: Name: NameFull: Kang, Qing IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 03605442 Numbering: – Type: volume Value: 303 Titles: – TitleFull: Energy Type: main |
| ResultId | 1 |
